[Kunlun] PR1:Support one Kunlun card training in parallel executor (#29337)

paddle/fluid/framework/details/broadcast_op_handle_test.h

@@ -33,6 +33,8 @@ struct VarHandle;
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 
+using UseDevice = paddle::framework::details::ExecutionStrategy::UseDevice;
+
 // test data amount
 const f::DDim kDims = {20, 20};
 
@@ -273,7 +275,8 @@ struct TestBroadcastOpHandle {
     f::LoD lod{{0, 10, 20}};
     auto send_vector = InitLoDTensor("input", input_scope_idx, lod);
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t j = 0; j < place_list_.size(); ++j) {
@@ -287,7 +290,8 @@ struct TestBroadcastOpHandle {
     int height = static_cast<int>(kDims[0] * 2);
     auto send_vector = InitSelectedRows("input", input_scope_idx, rows, height);
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t j = 0; j < place_list_.size(); ++j) {

paddle/fluid/framework/details/execution_strategy.h

@@ -21,10 +21,15 @@ namespace details {
 
 struct ExecutionStrategy {
   enum ExecutorType { kDefault = 0, kExperimental = 1 };
+  enum UseDevice {
+    kCPU = 0,
+    kCUDA = 1,
+    kXPU = 2,
+  };
 
   // num_threads indicates the size of thread pool.
   size_t num_threads_{0};
-  bool use_cuda_{true};
+  UseDevice use_device_{kCUDA};
   // Note that allow_op_delay is invalid now.
   bool allow_op_delay_{false};
   // num_iteration_per_drop_scope indicates how many

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -330,7 +330,7 @@ bool FastThreadedSSAGraphExecutor::RunOpSync(OpHandleBase *op) {
   try {
     VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
     if (LIKELY(!strategy_.dry_run_)) {
-      op->Run(strategy_.use_cuda_);
+      op->Run(strategy_.use_device_);
     }
     VLOG(10) << op << " " << op->Name() << " Done ";
     return true;

paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc

@@ -32,6 +32,7 @@ namespace framework {
 namespace details {
 
 struct VarHandle;
+using UseDevice = paddle::framework::details::ExecutionStrategy::UseDevice;
 
 struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
   std::vector<std::string> out_varnames_;
@@ -108,7 +109,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
           InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
     }
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
@@ -131,7 +133,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
                                              rows, height, val_scalar));
     }
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {

paddle/fluid/framework/details/gather_op_handle_test.cc

@@ -27,6 +27,8 @@ struct DummyVarHandle;
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 
+using UseDevice = paddle::framework::details::ExecutionStrategy::UseDevice;
+
 // test data amount
 const f::DDim kDims = {20, 20};
 
@@ -171,7 +173,8 @@ struct TestGatherOpHandle {
     out_selected_rows->mutable_value()->ShareDataWith(
         in_selected_rows->value());
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
 

paddle/fluid/framework/details/op_handle_base.cc

@@ -85,13 +85,14 @@ void OpHandleBase::InitCUDA() {
 #endif
 }
 
-void OpHandleBase::Run(bool use_cuda) {
+void OpHandleBase::Run(ExecutionStrategy::UseDevice use_device) {
 #ifdef PADDLE_WITH_CUDA
-  if (events_.empty() && use_cuda && dev_ctxes_.size() > 0) {
+  if (events_.empty() && use_device == ExecutionStrategy::UseDevice::kCUDA &&
+      dev_ctxes_.size() > 0) {
     InitCUDA();
   }
 #else
-  PADDLE_ENFORCE_EQ(use_cuda, false,
+  PADDLE_ENFORCE_NE(use_device, ExecutionStrategy::UseDevice::kCUDA,
                     platform::errors::InvalidArgument(
                         "Argument use_cuda should be false when Paddle is not "
                         "compiled with CUDA."));

paddle/fluid/framework/details/op_handle_base.h

@@ -19,6 +19,7 @@
 #include <unordered_set>
 #include <vector>
 
+#include "paddle/fluid/framework/details/execution_strategy.h"
 #include "paddle/fluid/framework/details/var_handle.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/platform/device_context.h"
@@ -71,7 +72,7 @@ class OpHandleBase {
 
   virtual std::string Name() const = 0;
 
-  void Run(bool use_cuda);
+  void Run(ExecutionStrategy::UseDevice use_device);
 
   virtual void RecordWaitEventOnCtx(platform::DeviceContext *waited_ctx);
 

paddle/fluid/framework/details/reduce_op_handle_test.cc

@@ -25,6 +25,8 @@ namespace details {
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 
+using UseDevice = paddle::framework::details::ExecutionStrategy::UseDevice;
+
 // test data amount
 const f::DDim kDims = {20, 20};
 
@@ -196,7 +198,8 @@ struct TestReduceOpHandle {
     out_selected_rows->mutable_value()->ShareDataWith(
         in_selected_rows->value());
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
 
@@ -260,7 +263,8 @@ struct TestReduceOpHandle {
 
     out_lodtensor->ShareDataWith(in_lodtensor);
 
-    op_handle_->Run(false);
+    UseDevice use_device = UseDevice::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
 

paddle/fluid/framework/details/scale_loss_grad_op_handle.cc

@@ -58,6 +58,17 @@ struct ScaleLossGradFunctor {
     auto *out_data = out_->mutable_data<OutT>(place_);
     if (platform::is_cpu_place(place_)) {
       *out_data = static_cast<OutT>(coeff_);
+    } else if (platform::is_xpu_place(place_)) {
+#if defined(PADDLE_WITH_XPU)
+      OutT cast_coeff = static_cast<OutT>(coeff_);
+      memory::Copy(BOOST_GET_CONST(platform::XPUPlace, place_), out_data,
+                   platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_));
+      VLOG(10) << place_ << "RUN Scale loss grad op";
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use XPU device since it's not compiled with XPU,"
+          "Please recompile or reinstall Paddle with XPU support."));
+#endif
     } else {
 #ifdef PADDLE_WITH_CUDA
       OutT cast_coeff = static_cast<OutT>(coeff_);
@@ -66,7 +77,10 @@ struct ScaleLossGradFunctor {
                    platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_),
                    stream);
       VLOG(10) << place_ << "RUN Scale loss grad op";
-
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use CUDA device since it's not compiled with CUDA,"
+          "Please recompile or reinstall Paddle with GPU support."));
 #endif
     }
   }

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -348,7 +348,7 @@ bool ThreadedSSAGraphExecutor::RunOpSync(OpHandleBase *op) {
   try {
     VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
     if (LIKELY(!strategy_.dry_run_)) {
-      op->Run(strategy_.use_cuda_);
+      op->Run(strategy_.use_device_);
     }
     VLOG(10) << op << " " << op->Name() << " Done ";
     return true;

paddle/fluid/framework/ir/memory_optimize_pass/test_reference_count_pass_last_lived_ops.cc

@@ -88,7 +88,8 @@ class ReferenceCountPassTestHelper {
     FLAGS_eager_delete_tensor_gb = -1;
 
     details::ExecutionStrategy exec_strategy;
-    exec_strategy.use_cuda_ = use_cuda;
+    exec_strategy.use_device_ =
+        use_cuda ? (ExecutionStrategy::kCUDA) : (ExecutionStrategy::kCPU);
 
     executor_.reset(new ParallelExecutor(CreatePlaces(1, use_cuda), {}, "",
                                          &scope_, {}, exec_strategy,

paddle/fluid/framework/parallel_executor.cc

@@ -63,6 +63,8 @@ static bool gProfileStarted = false;
 std::once_flag p2p_init_flag;
 #endif
 
+using UseDevice = paddle::framework::details::ExecutionStrategy::UseDevice;
+
 class ParallelExecutorPrivate {
  public:
   ParallelExecutorPrivate(const std::vector<platform::Place> &places,
@@ -93,6 +95,8 @@ class ParallelExecutorPrivate {
     }
   }
 
+  bool IsUseCUDA(UseDevice use_device);
+
   void SetHasFeed(size_t dev_idx, bool has_feed = true);
 
   bool AllowPartialFeed() const;
@@ -286,7 +290,7 @@ class ParallelExecutorPrivate {
   platform::NCCLCommunicator *nccl_ctxs_{nullptr};
 #endif
   bool own_local_scope_;
-  bool use_cuda_;
+  UseDevice use_device_;
   bool use_all_reduce_;
   size_t nranks_;
 
@@ -296,6 +300,10 @@ class ParallelExecutorPrivate {
   details::ParallelSSAGraphExecutor *inference_executor_{nullptr};
 };
 
+bool ParallelExecutorPrivate::IsUseCUDA(UseDevice use_device) {
+  return use_device == UseDevice::kCUDA;
+}
+
 void ParallelExecutorPrivate::SetHasFeed(size_t dev_idx, bool has_feed) {
   if (inference_executor_) {
     inference_executor_->SetHasFeed(dev_idx, has_feed);
@@ -340,7 +348,7 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
     auto addto_pass = ir::PassRegistry::Instance().Get("inplace_addto_op_pass");
     addto_pass->SetNotOwned(ir::kMemOptVarInfoMapList, &mem_opt_var_infos_);
     addto_pass->SetNotOwned(ir::kLastLiveOpsOfVars, &last_live_ops_of_vars);
-    addto_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    addto_pass->Set(ir::kUseCuda, new bool(use_device_ == UseDevice::kCUDA));
     VLOG(10) << "Start to apply inplace_addto_op_pass";
     graph = addto_pass->Apply(graph);
     VLOG(10) << "inplace_addto_op_pass Applied";
@@ -351,7 +359,7 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
         ir::PassRegistry::Instance().Get("buffer_shared_inplace_pass");
     inplace_pass->SetNotOwned(ir::kMemOptVarInfoMapList, &mem_opt_var_infos_);
     inplace_pass->SetNotOwned(ir::kLastLiveOpsOfVars, &last_live_ops_of_vars);
-    inplace_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    inplace_pass->Set(ir::kUseCuda, new bool(use_device_ == UseDevice::kCUDA));
     VLOG(10) << "Start to apply buffer_shared_inplace_pass";
     graph = inplace_pass->Apply(graph);
     VLOG(10) << "buffer_shared_inplace_pass Applied";
@@ -366,7 +374,8 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
                                             &mem_opt_var_infos_);
     cross_op_memory_reuse_pass->SetNotOwned(ir::kLastLiveOpsOfVars,
                                             &last_live_ops_of_vars);
-    cross_op_memory_reuse_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    cross_op_memory_reuse_pass->Set(ir::kUseCuda,
+                                    new bool(use_device_ == UseDevice::kCUDA));
     VLOG(10) << "Start to apply buffer_shared_cross_op_memory_reuse_pass";
     graph = cross_op_memory_reuse_pass->Apply(graph);
     VLOG(10) << "buffer_shared_cross_op_memory_reuse_pass Applied";
@@ -386,8 +395,8 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
       continue;
     }
     std::unique_ptr<GarbageCollector> gc;
-#ifdef PADDLE_WITH_CUDA
     if (platform::is_gpu_place(place)) {
+#ifdef PADDLE_WITH_CUDA
       if (IsFastEagerDeletionModeEnabled()) {
         gc.reset(new UnsafeFastGPUGarbageCollector(
             BOOST_GET_CONST(platform::CUDAPlace, place), max_memory_size));
@@ -396,9 +405,22 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
             BOOST_GET_CONST(platform::CUDAPlace, place), max_memory_size));
       }
       VLOG(10) << "Created " << i << "-th GarbageCollector at " << place;
-    } else {
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use CUDA device since it's not compiled with CUDA,"
+          "Please recompile or reinstall Paddle with GPU support."));
+#endif
+    } else if (platform::is_xpu_place(place)) {
+#if defined(PADDLE_WITH_XPU)
+      gc.reset(new XPUGarbageCollector(
+          BOOST_GET_CONST(platform::XPUPlace, place), max_memory_size));
+      VLOG(10) << "Created " << i << "-th GarbageCollector at " << place;
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use XPU device since it's not compiled with XPU,"
+          "Please recompile or reinstall Paddle with XPU support."));
 #endif
-      if (platform::is_cpu_place(place)) {
+    } else if (platform::is_cpu_place(place)) {
       gc.reset(new CPUGarbageCollector(
           BOOST_GET_CONST(platform::CPUPlace, place), max_memory_size));
       VLOG(10) << "Created GarbageCollector at " << place;
@@ -406,10 +428,6 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
       PADDLE_THROW(platform::errors::PreconditionNotMet(
           "Unsupported place for garbage collection"));
     }
-#ifdef PADDLE_WITH_CUDA
-    }
-#endif
-
     gcs_.emplace(place, std::move(gc));
   }
 
@@ -510,13 +528,10 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
                                    const BuildStrategy &build_strategy,
                                    ir::Graph *graph)
     : member_(new ParallelExecutorPrivate(places, scope)) {
-  PADDLE_ENFORCE(places.size() > 0 && !is_xpu_place(places[0]),
-                 platform::errors::Unavailable(
-                     "XPU is not supported in ParallelExecutor"));
   InitP2P(places);
   ir::InitReaderQueueDeviceCount(graph, *(member_->global_scope_),
                                  member_->places_.size());
-  member_->use_cuda_ = exec_strategy.use_cuda_;
+  member_->use_device_ = exec_strategy.use_device_;
   member_->build_strategy_ = build_strategy;
   member_->use_all_reduce_ = member_->build_strategy_.reduce_ ==
                              BuildStrategy::ReduceStrategy::kAllReduce;
@@ -529,7 +544,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     member_->use_all_reduce_ = true;
   }
 #if defined(PADDLE_WITH_CUDA) && defined(_WIN32)
-  if (member_->use_cuda_) {
+  if (member_->IsUseCUDA(member_->use_device_)) {
     PADDLE_ENFORCE_EQ(
         places.size(), 1,
         platform::errors::Unavailable("Windows can support Single GPU only."));
@@ -537,7 +552,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
 #endif
 
 #if defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_NCCL)
-  if (member_->use_cuda_) {
+  if (member_->IsUseCUDA(member_->use_device_)) {
     PADDLE_ENFORCE_EQ(
         places.size(), 1,
         platform::errors::PermissionDenied(
@@ -548,10 +563,19 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
   }
 #endif
 
+  std::string device_name;
+  if (member_->use_device_ == UseDevice::kCPU) {
+    device_name = "CPU";
+  } else if (member_->use_device_ == UseDevice::kCUDA) {
+    device_name = "CUDA";
+  } else {
+    device_name = "XPU";
+  }
+
   VLOG(1) << string::Sprintf(
       "The Program will be executed on %s using ParallelExecutor, %lu "
       "cards are used, so %lu programs are executed in parallel.",
-      (member_->use_cuda_ ? "CUDA" : "CPU"), places.size(), places.size());
+      device_name, places.size(), places.size());
 
   // Step 1. Bcast the bcast_vars to devs.
   // Create local scopes
@@ -575,7 +599,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
 
   std::vector<ir::Graph *> graphs;
   if (member_->build_strategy_.async_mode_) {
-    PADDLE_ENFORCE_EQ(member_->use_cuda_, false,
+    PADDLE_ENFORCE_EQ(member_->IsUseCUDA(member_->use_device_), false,
                       platform::errors::Unavailable(
                           "gpu mode does not support async_mode_ now!"));
     graphs.push_back(graph);
@@ -598,7 +622,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
               << "you can force it off by env FLAGS_enable_parallel_graph=0";
   }
 
-  if (member_->use_cuda_ && member_->nranks_ > 1) {
+  if (member_->IsUseCUDA(member_->use_device_) && member_->nranks_ > 1) {
 #if defined(PADDLE_WITH_NCCL)
     member_->InitOrGetNCCLCommunicator(scope, &member_->build_strategy_);
 
@@ -647,36 +671,39 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     VLOG(3) << "use local async mode";
     graph = member_->build_strategy_.Apply(
         graph, {member_->places_[0]}, loss_var_name,
-        {member_->local_scopes_[0]}, 1, member_->use_cuda_,
+        {member_->local_scopes_[0]}, 1,
-        member_->nccl_ctxs_);
+        member_->IsUseCUDA(member_->use_device_), member_->nccl_ctxs_);
     for (size_t i = 1; i < member_->places_.size(); ++i) {
       graphs[i] = member_->build_strategy_.Apply(
           graphs[i], {member_->places_[i]}, loss_var_name,
-          {member_->local_scopes_[i]}, 1, member_->use_cuda_,
+          {member_->local_scopes_[i]}, 1,
-          member_->nccl_ctxs_);
+          member_->IsUseCUDA(member_->use_device_), member_->nccl_ctxs_);
       async_graphs[i] = graphs[i];
     }
   } else {
     graph = member_->build_strategy_.Apply(
         graph, member_->places_, loss_var_name, member_->local_scopes_,
-        member_->nranks_, member_->use_cuda_, member_->nccl_ctxs_);
+        member_->nranks_, member_->IsUseCUDA(member_->use_device_),
+        member_->nccl_ctxs_);
   }
 #else
   if (member_->build_strategy_.async_mode_) {
     VLOG(3) << "use local async mode";
     graph = member_->build_strategy_.Apply(
         graph, {member_->places_[0]}, loss_var_name,
-        {member_->local_scopes_[0]}, 1, member_->use_cuda_);
+        {member_->local_scopes_[0]}, 1,
+        member_->IsUseCUDA(member_->use_device_));
     for (size_t i = 1; i < member_->places_.size(); ++i) {
       graphs[i] = member_->build_strategy_.Apply(
           graphs[i], {member_->places_[i]}, loss_var_name,
-          {member_->local_scopes_[i]}, 1, member_->use_cuda_);
+          {member_->local_scopes_[i]}, 1,
+          member_->IsUseCUDA(member_->use_device_));
       async_graphs[i] = graphs[i];
     }
   } else {
     graph = member_->build_strategy_.Apply(
         graph, member_->places_, loss_var_name, member_->local_scopes_,
-        member_->nranks_, member_->use_cuda_);
+        member_->nranks_, member_->IsUseCUDA(member_->use_device_));
   }
 #endif
 
@@ -874,7 +901,8 @@ void ParallelExecutor::BCastParamsToDevices(
         // FIXME(zcd): LR_DECAY_COUNTER should not be shared. This is a hot fix.
         if (member_->build_strategy_.async_mode_) {
           share_memory();
-        } else if (member_->use_all_reduce_ || member_->use_cuda_ ||
+        } else if (member_->use_all_reduce_ ||
+                   member_->IsUseCUDA(member_->use_device_) ||
                    var == "@LR_DECAY_COUNTER@") {
           copy_memory();
         } else {
@@ -1105,7 +1133,7 @@ bool ParallelExecutor::EnableParallelGraphExecution(
     }
   }
 
-  if (!member_->use_all_reduce_ || !member_->use_cuda_) {
+  if (!member_->use_all_reduce_ || !member_->IsUseCUDA(member_->use_device_)) {
     if (build_strategy.enable_sequential_execution_ ||
         exec_strategy.type_ == ExecutionStrategy::ExecutorType::kExperimental) {
       enable_parallel_graph = false;

paddle/fluid/platform/device_context.cc

@@ -29,10 +29,12 @@ namespace memory {
 
 AllocationPtr Alloc(const platform::DeviceContext& dev_ctx, size_t size) {
   auto place = dev_ctx.GetPlace();
-#ifdef PADDLE_WITH_CUDA
+  if (size == 0) {
-  if (size == 0 || !platform::is_gpu_place(place)) {
     return Alloc(place, size);
   }
+
+  if (platform::is_gpu_place(place)) {
+#ifdef PADDLE_WITH_CUDA
     auto* default_dev_ctx = static_cast<platform::CUDADeviceContext*>(
         platform::DeviceContextPool::Instance().Get(place));
     auto& desired_dev_ctx =
@@ -44,8 +46,22 @@ AllocationPtr Alloc(const platform::DeviceContext& dev_ctx, size_t size) {
           desired_dev_ctx, size);
     }
 #else
+    PADDLE_THROW(platform::errors::PermissionDenied(
+        "Paddle can't use CUDA device since it's not compiled with CUDA,"
+        "Please recompile or reinstall Paddle with GPU support."));
+#endif
+  } else if (platform::is_xpu_place(place)) {
+#ifdef PADDLE_WITH_XPU
+    // TODO(liuyuhui): Consider xpu stream later
     return Alloc(place, size);
+#else
+    PADDLE_THROW(platform::errors::PermissionDenied(
+        "Paddle can't use XPU device since it's not compiled with XPU,"
+        "Please recompile or reinstall Paddle with XPU support."));
 #endif
+  } else {
+    return Alloc(place, size);
+  }
 }
 
 }  // namespace memory

paddle/fluid/pybind/pybind.cc

@@ -1492,7 +1492,9 @@ All parameter, weight, gradient are variables in Paddle.
 #endif
       .def("__repr__", string::to_string<const platform::XPUPlace &>)
       .def("__str__", string::to_string<const platform::XPUPlace &>);
-
+#ifdef PADDLE_WITH_XPU
+  m.def("get_xpu_device_count", platform::GetXPUDeviceCount);
+#endif
   py::class_<paddle::platform::CPUPlace>(m, "CPUPlace", R"DOC(
     CPUPlace is a descriptor of a device.
     It represents a CPU device on which a tensor will be allocated and a model will run.
@@ -2077,6 +2079,11 @@ All parameter, weight, gradient are variables in Paddle.
                                               exec_strategy=exec_strategy)
         )DOC");
 
+  py::enum_<ExecutionStrategy::UseDevice>(exec_strategy, "UseDevice")
+      .value("CPU", ExecutionStrategy::UseDevice::kCPU)
+      .value("CUDA", ExecutionStrategy::UseDevice::kCUDA)
+      .value("XPU", ExecutionStrategy::UseDevice::kXPU);
+
   exec_strategy.def(py::init())
       .def_property(
           "num_threads",
@@ -2107,14 +2114,12 @@ All parameter, weight, gradient are variables in Paddle.
                     exec_strategy.num_threads = 4
             )DOC")
       .def_property(
-          "use_cuda",
+          "_use_device",
-          [](const ExecutionStrategy &self) { return self.use_cuda_; },
+          [](const ExecutionStrategy &self) { return self.use_device_; },
-          [](ExecutionStrategy &self, bool use_cuda) {
+          [](ExecutionStrategy &self, ExecutionStrategy::UseDevice use_device) {
-            self.use_cuda_ = use_cuda;
+            self.use_device_ = use_device;
-          })  // FIXME(chengduo): Doesn't add doc for 'use_cuda', use_cuda may
+          })  // NOTE(liuyuhui): Doesn't add doc for 'use_device', because
-      // make user confuse, because ParallelExecutor has a parameter named
+              // use_device isn‘t exposed to users.
-      // 'use_cuda' too, in current implementation, ParallelExecutor's
-      // 'use_cuda' will rewrite ExecutionStrategy's 'use_cuda'.
       .def_property(
           "allow_op_delay",
           [](const ExecutionStrategy &self) { return self.allow_op_delay_; },

python/paddle/fluid/compiler.py

@@ -18,7 +18,7 @@ import six
 import sys
 from .. import compat as cpt
 from . import framework
-from .framework import cuda_places, cpu_places
+from .framework import cuda_places, cpu_places, xpu_places
 
 from . import core
 
@@ -316,7 +316,7 @@ class CompiledProgram(object):
             "Subclass of CompiledProgram should implement _with_distributed method."
         )
 
-    def _compile_data_parallel(self, places, use_cuda=False, scope=None):
+    def _compile_data_parallel(self, places, use_device, scope=None):
         if self._share_vars_from:
             if scope:
                 sys.stderr.write("share_vars_from is set, scope is ignored.\n")
@@ -342,16 +342,23 @@ class CompiledProgram(object):
 
         if self._exec_strategy is None:
             self._exec_strategy = ExecutionStrategy()
-        self._exec_strategy.use_cuda = use_cuda
+        self._exec_strategy._use_device = use_device
 
         if self._exec_strategy.num_threads == 0:
-            if self._exec_strategy.use_cuda:
+            if self._exec_strategy._use_device == ExecutionStrategy.UseDevice.CUDA:
                 # Experiments on se-resnext shows that too many threads hurt
                 # performance. Worth tunning for other models in the future.
                 self._exec_strategy.num_threads = len(places) * 4
+            elif self._exec_strategy._use_device == ExecutionStrategy.UseDevice.XPU:
+                # Currently only single thread is supported in Kunlun XPU.
+                self._exec_strategy.num_threads = 1
             else:
                 self._exec_strategy.num_threads = len(places) * 2
 
+        if self._exec_strategy._use_device == ExecutionStrategy.UseDevice.XPU:
+            assert self._exec_strategy.num_threads == 1, \
+                "Currently only single thread is supported in Kunlun XPU."
+
         if self._build_strategy.num_trainers > 1:
             assert self._is_data_parallel, \
                 "If you use multi-trainer to train the model, you should use "\
@@ -377,7 +384,7 @@ class CompiledProgram(object):
             self._build_strategy.enable_sequential_execution = True
 
         if self._program is not None and self._program._enable_dgc:
-            assert use_cuda, "DGC only used under CUDA environment."
+            assert self._exec_strategy._use_device == ExecutionStrategy.UseDevice.CUDA, "DGC only used under CUDA environment."
             assert self._build_strategy.num_trainers * len(
                 places) > 1, "DGC is not avaliable for single card training."
             assert self._build_strategy.reduce_strategy == BuildStrategy.ReduceStrategy.AllReduce, "DGC \
@@ -447,11 +454,14 @@ class CompiledProgram(object):
                 raise NotImplementedError(
                     "If optimizer is used in control flow, "
                     "training on multi-places is not supported now.")
-
+            if isinstance(self._place, core.CUDAPlace):
+                use_device = ExecutionStrategy.UseDevice.CUDA
+            elif isinstance(self._place, core.XPUPlace):
+                use_device = ExecutionStrategy.UseDevice.XPU
+            else:
+                use_device = ExecutionStrategy.UseDevice.CPU
             self._executor = self._compile_data_parallel(
-                use_cuda=isinstance(self._place, core.CUDAPlace),
+                use_device=use_device, scope=self._scope, places=self._places)
-                scope=self._scope,
-                places=self._places)
         return self
 
     def _get_places(self, place, place_list):
@@ -461,7 +471,11 @@ class CompiledProgram(object):
                 assert p._type() == place._type(), \
                     "Place type not match. You may set wrong type of places."
         else:
-            place_list = cuda_places() if isinstance(
+            if isinstance(place, core.CUDAPlace):
-                place, core.CUDAPlace) else cpu_places()
+                place_list = cuda_places()
+            elif isinstance(place, core.XPUPlace):
+                place_list = xpu_places()
+            else:
+                place_list = cpu_places()
         assert place_list, "No places for execution."
         return place_list

python/paddle/fluid/framework.py

@@ -47,6 +47,7 @@ __all__ = [
     'name_scope',
     'cuda_places',
     'cpu_places',
+    'xpu_places',
     'cuda_pinned_places',
     'in_dygraph_mode',
     'is_compiled_with_cuda',
@@ -354,6 +355,15 @@ def _cuda_ids():
     return device_ids
 
 
+def _xpu_ids():
+    xpus_env = os.getenv("FLAGS_selected_xpus")
+    if xpus_env:
+        device_ids = [int(s) for s in xpus_env.split(",")]
+    else:
+        device_ids = six.moves.range(core.get_xpu_device_count())
+    return device_ids
+
+
 def is_compiled_with_xpu():
     """
     Whether this whl package can be used to run the model on XPU.
@@ -430,6 +440,43 @@ def cuda_places(device_ids=None):
     return [core.CUDAPlace(dev_id) for dev_id in device_ids]
 
 
+def xpu_places(device_ids=None):
+    """
+    **Note**:
+        For multi-card tasks, please use `FLAGS_selected_xpus` environment variable to set the visible XPU device.
+    This function creates a list of :code:`paddle.XPUPlace` objects.
+    If :code:`device_ids` is None, environment variable of
+    :code:`FLAGS_selected_xpus` would be checked first. For example, if
+    :code:`FLAGS_selected_xpus=0,1,2`, the returned list would
+    be [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
+    If :code:`FLAGS_selected_xpus` is not set, all visible
+    xpu places would be returned.
+    If :code:`device_ids` is not None, it should be the device
+    ids of XPUs. For example, if :code:`device_ids=[0,1,2]`,
+    the returned list would be 
+    [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
+    
+    Parameters:
+        device_ids (list or tuple of int, optional): list of XPU device ids.
+    Returns:
+        list of paddle.XPUPlace: Created XPU place list.
+    Examples:
+        .. code-block:: python
+            import paddle
+            import paddle.static as static
+            
+            paddle.enable_static()
+            xpu_places = static.xpu_places()
+    """
+    assert core.is_compiled_with_xpu(), \
+        "Not compiled with XPU"
+    if device_ids is None:
+        device_ids = _xpu_ids()
+    elif not isinstance(device_ids, (list, tuple)):
+        device_ids = [device_ids]
+    return [core.XPUPlace(dev_id) for dev_id in device_ids]
+
+
 def cpu_places(device_count=None):
     """
     This function creates a list of :code:`paddle.CPUPlace` objects, and returns the created list.

python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py

@@ -75,7 +75,7 @@ class TestIrMemoryOptimizeIfElseOp(unittest.TestCase):
             exe = Executor(place)
 
             exec_strategy = fluid.ExecutionStrategy()
-            exec_strategy.use_cuda = use_cuda
+            exec_strategy._use_device = fluid.ExecutionStrategy.UseDevice.CUDA if use_cuda else fluid.ExecutionStrategy.UseDevice.CPU
 
             build_strategy = fluid.BuildStrategy()
             build_strategy.memory_optimize = use_mem_opt

python/paddle/fluid/tests/unittests/xpu/test_xpu_place.py

@@ -0,0 +1,47 @@
+#   copyright (c) 2020 paddlepaddle authors. all rights reserved.
+#
+# licensed under the apache license, version 2.0 (the "license");
+# you may not use this file except in compliance with the license.
+# you may obtain a copy of the license at
+#
+#     http://www.apache.org/licenses/license-2.0
+#
+# unless required by applicable law or agreed to in writing, software
+# distributed under the license is distributed on an "as is" basis,
+# without warranties or conditions of any kind, either express or implied.
+# see the license for the specific language governing permissions and
+# limitations under the license.
+
+from __future__ import print_function
+
+import unittest
+import os
+import paddle
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid import core
+import paddle.static as static
+
+
+class Test_XPU_Places(unittest.TestCase):
+    def assert_places_equal(self, places0, places1):
+        self.assertEqual(len(places0), len(places1))
+        for place0, place1 in zip(places0, places1):
+            self.assertEqual(type(place0), type(place1))
+            self.assertEqual(place0.get_device_id(), place1.get_device_id())
+
+    def test_check_preset_envs(self):
+        if core.is_compiled_with_xpu():
+            os.environ["FLAGS_selected_xpus"] = "0"
+            place_list = static.xpu_places()
+            self.assert_places_equal([fluid.XPUPlace(0)], place_list)
+
+    def test_check_no_preset_envs(self):
+        if core.is_compiled_with_xpu():
+            place_list = static.xpu_places(0)
+            self.assert_places_equal([fluid.XPUPlace(0)], place_list)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()

python/paddle/static/__init__.py

@@ -20,7 +20,7 @@ __all__ = [
     'default_main_program', 'default_startup_program', 'Program', 'data',
     'InputSpec', 'save', 'load', 'save_inference_model', 'load_inference_model',
     'load_program_state', 'set_program_state', 'cpu_places', 'cuda_places',
-    'Variable'
+    'xpu_places', 'Variable'
 ]
 
 from . import nn
@@ -45,6 +45,7 @@ from ..fluid.framework import name_scope  #DEFINE_ALIAS
 from ..fluid.framework import program_guard  #DEFINE_ALIAS
 from ..fluid.framework import cpu_places  #DEFINE_ALIAS
 from ..fluid.framework import cuda_places  #DEFINE_ALIAS
+from ..fluid.framework import xpu_places  #DEFINE_ALIAS
 from ..fluid.framework import Variable  #DEFINE_ALIAS
 from ..fluid.layers.control_flow import Print  #DEFINE_ALIAS
 from ..fluid.layers.nn import py_func  #DEFINE_ALIAS

tools/wlist.json

@@ -413,7 +413,8 @@
         "CRFDecoding.forward",
         "SequenceTagging.forward",
         "XPUPlace",
-        "is_compiled_with_xpu"
+        "is_compiled_with_xpu",
+        "xpu_places"
     ],
     "gpu_not_white":[
         "deformable_conv",